Describing Data Graphically with R

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layout: true
  
<div class="my-footer">
<span>
<a href="https://psychmethods.github.io/coursenotes/" target="_blank">Methods in Psychological Research</a>
</span>
</div>

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# Describing Data

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## Hans Rosling

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# Summarize

- Transform a pile of numbers into a summary
- Descriptive Statistics
    - Distribution of a variable is a table/graph showing the categories/values of outcomes and their frequency/percentage of occurrence
    - Exploratory Data Analysis (Tukey, 1977)
]
.pull-right[

![](data:image/png;base64,#../img/numberpile.png)
]

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# Exploratory Data Analysis
.pull-left[
- Tukey (1977)
    - EDA
    - Graphical Data Analysis
    - Numbers as summaries
    - Emphasized Robust Statistics
]
--
.pull-right[
![](data:image/png;base64,#../img/tukey.png)
]

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# Descriptive Statistics
- Examples
    - Tables
    - Graphs
    - Summary Statistics

---

# Tables

- Woodbridge (1845)
    
<img src="data:image/png;base64,#../img/woodbridge1845.png" width="100%" style="display: block; margin: auto;" />

---

# Graphs

-  Minard (1869)
    
<img src="data:image/png;base64,#../img/minard.png" width="70%" style="display: block; margin: auto;" />

---

# Examples

- Summary Statistics
    - Measures of Central Tendency
    - Measures of Spread
    
<img src="data:image/png;base64,#../img/centralbears.jpg" width="30%" style="display: block; margin: auto;" />

---

# Categorical Variable Displays (Nominal, Ordinal)

- Frequency Distribution Graphs
  - Bar Chart
  - Pie Chart
--

- Quantitative Variables
  - Histograms
  - Stem plots
--

- Time Plots

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# Frequency distribution graph

.pull-left[
- Bar Chart
    - Graphs of variables with categories of outcomes on the x axis; and the frequency or percent of each category on the Y axis.
 
]
.pull-right[

]

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# Bar Graph/Chart

```r
# Bar chart
library(car)
counts <- table(mtcars$gear)
```
]
.pull-right.small[

```r
barplot(counts, main="Car Distribution",
  	xlab="Number of Gears")
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-8-1.png" width="90%" style="display: block; margin: auto;" />
]
---

# Stacked Bar Chart
.small.pull-left[

```r
df <- data.frame(
  group = c("Male", 
            "Female", 
            "Child"),
  value = c(25, 25, 50))
head(df)
```

```
##    group value
## 1   Male    25
## 2 Female    25
## 3  Child    50
```
]
.pull-right.small[

```r
library(ggplot2)
bp <- ggplot(df, aes(x="",
                     y=value,
                     fill = group))+ 
  geom_bar(width = 1,stat = "identity")
bp
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-10-1.png" width="90%" style="display: block; margin: auto;" />
]
---

# Pie Chart
.pull-left[
- Graphs of variables with categories of outcomes as frequency or percent of each category in the pie.
]

.pull-right[
<img src="data:image/png;base64,#../img/pieplay.jpg" width="90%" height="40%" style="display: block; margin: auto;" />
.footnote["A pie chart showing each state in the United States, part of Playfair's translation of A Statistical Account of the United States of America by D. F. Donnant."
]]

---

# Pie chart

```r
slices <- c(10,12,
            4,16, 8)
lbls <- c("US", "UK",
          "Australia",
          "Germany",
          "France")
```
]
.pull-right-wide[

```r
pie(slices, labels = lbls,
    main="Pie Chart of Countries")
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-13-1.png" width="90%" style="display: block; margin: auto;" />
]
---

# Example 2

.small.pull-left[

```r
mytable <- table(iris$Species)
lbls <- paste(names(mytable), "\n", mytable, sep="")
pie(mytable, labels = lbls,
		main="Pie Chart of Species\n (with sample sizes)")
```

<img src="data:image/png;base64,#descriptive_files/figure-html/example2-1.png" width="90%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-14-1.png" width="65%" style="display: block; margin: auto;" />
]

---

# Convert Bar Chart into Pie Chart

.small.pull-left[

```r
pie <- bp + coord_polar("y", start=0)
pie
```

```r
pie + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))
```

<img src="data:image/png;base64,#descriptive_files/figure-html/polar-2.png" width="90%" style="display: block; margin: auto;" />
]
.pull-right[
<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-15-1.png" width="65%" style="display: block; margin: auto;" /><img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-15-2.png" width="65%" style="display: block; margin: auto;" />
]

[Additional Resources](http://www.sthda.com/english/wiki/ggplot2-pie-chart-quick-start-guide-r-software-and-data-visualization)

---

# Quantitative Variables

- Interval or Ratio Scales
    - Histograms
    - Stem plots
    - Time plots

---

# Histogram 
- A histogram is a graphical representation of the distribution of numerical data.
    - Approximates a probability distribution
    - First described by  Pearson in 1895.
    
---

# Histogram

```r
library(MASS) # load library
variable<-cats$Bwt
hist(variable)
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-16-1.png" width="90%" style="display: block; margin: auto;" />
]

```r
 #Convert to Imperial
variable<-variable*2.2

hist(variable)
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-17-1.png" width="90%" style="display: block; margin: auto;" />
]
---

# Stemplot

- Sometimes called a stem and leaf diagram

```r
# Stem and Leaf plot
stem(faithful$eruptions,scale=1)
```

```
## 
##   The decimal point is 1 digit(s) to the left of the |
## 
##   16 | 070355555588
##   18 | 000022233333335577777777888822335777888
##   20 | 00002223378800035778
##   22 | 0002335578023578
##   24 | 00228
##   26 | 23
##   28 | 080
##   30 | 7
##   32 | 2337
##   34 | 250077
##   36 | 0000823577
##   38 | 2333335582225577
##   40 | 0000003357788888002233555577778
##   42 | 03335555778800233333555577778
##   44 | 02222335557780000000023333357778888
##   46 | 0000233357700000023578
##   48 | 00000022335800333
##   50 | 0370
```

---

# Time Plots

<img src="data:image/png;base64,#../img/minard.png" width="30%" style="display: block; margin: auto;" />
--

- Edward Tufte has said that Minard's plot:

> "may well be the best statistical graphic ever drawn"

- It packs a ton of information into one dense figure.

---

# Time Plots

---

- The plot contains six variables, each mapped to a different aesthetic:

| Information                           | Aesthetic       |
|---------------------------------------|-----------------|
| Size of Napoleon's Grande Armée       | Width of path   |
| Longitude of the army's position      | x-axis          |
| Latitude of the army's position       | y-axis          |
| Direction of the army's movement      | Color of path   |
| Date of points along retreat path     | Text below plot |
| Temperature during the army's retreat | Line below plot |

---

# Recreation in R

- This plot has been recreated in R by:
    - [Andrew Heiss](https://www.andrewheiss.com/blog/2017/08/10/exploring-minards-1812-plot-with-ggplot2/)
    - [Michael Friendly](http://www.datavis.ca/gallery/re-minard.php)
    - [Hadley Wickham](https://www.tandfonline.com/doi/suppl/10.1198/jcgs.2009.07098?scroll=top)

---

# Side by Side

.pull-left[
<img src="data:image/png;base64,#../img/minard.png" width="90%" style="display: block; margin: auto;" />
]

.pull-right[
<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-22-1.png" width="90%" style="display: block; margin: auto;" />
]

---

# More Accessible Resources

- [R Graph Catalog](http://shinyapps.stat.ubc.ca/r-graph-catalog/)
- [intRo](http://www.intro-stats.com/)
- .hand-pink[[Data Science for Psychologists](https://datascience4psych.github.io/DataScience4Psych/)]

---

Wrapping Up...

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# R Basics

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# R Basics

- R is a powerful language for statistical computing and graphics
- It's free, open-source, and has a large community of users and developers

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# R Installation and Setup

- Download R from CRAN (Comprehensive R Archive Network): https://cran.r-project.org/
- Choose the version for your operating system
- RStudio is a popular IDE for R, available at: https://www.rstudio.com/products/rstudio/download/

---

# R Console

- After installation, you can start R or RStudio
- You'll see a console where you can enter commands
- Try a simple calculation:

```r
2 + 3
```

```
## [1] 5
```

---

# Variable Assignment

- Use `<-` or `=` to assign values to variables

Variable names should be descriptive and use `under_scores` or `camelCase`

```r
x <- 10
y = 20
total_sum <- x + y
totalSum <- x + y
print(total_sum)
```

```
## [1] 30
```

---

# Basic Data Types

- Numeric: 1.5, 2, 3.14
- Character: "hello", 'world'
- Logical: TRUE, FALSE

---

# Vectors

- Create vectors using `c()` function
- Vectors can contain elements of the same type

```r
numeric_vector <- c(1, 2, 3, 4, 5)
character_vector <- c("a", "b", "c")
print(numeric_vector)
```

```
## [1] 1 2 3 4 5
```

---

# Basic Plotting in R

.pull-left[
- R has built-in plotting functions
- These are useful for quick visualizations
]
.pull-right[

```r
x <- 1:10
y <- x^2
plot(x, y, main="Simple Plot", xlab="X axis", ylab="Y axis")
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-27-1.png" width="90%" style="display: block; margin: auto;" />
]

---

# Using Functions

.pull-left-narrow[
- R has many built-in functions
- You can also create your own functions
]
.pull-right-wide[

```r
# Built-in function
mean(c(1, 2, 3, 4, 5))
```

```
## [1] 3
```

```r
# Custom function
square <- function(x) {
  return(x^2)
}
square(4)
```

```
## [1] 16
```
]

---

# Comments

- All text after the pound sign "#" within the same line is considered a comment.

> 1 + 1      # this is a comment 
[1] 2

---

# Getting Help

- R provides extensive documentation. 
- Use `?` or `help()` to get information about functions
- For example, entering `?c` or `help(c)` at the prompt gives documentation of the function `c` in R.
- Example:

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# Getting Help

> help(c)
If you are not sure about the name of the function you are looking for, you can perform a fuzzy search with the apropos function.

> apropos("nova") 
[1] "anova"                "anova.glm" 
   ....

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# Wrapping Up...